2010 spring BiS427 Computational Neuroscience

 

Synopsis 

"Computational Neuroscience" aims at introducing fundamental concepts and principles of neural information processes in the brain and its application to artificial intelligent systems for third-year undergraduate students. The topics studied in this class include the information processing of single neurons (e.g. action potentials and their series of spikes) and information encoding and decoding, information processes of the neuronal networks and neural ensembles in various parts of the brain. In addition, Hebbean learning, reinforcement learning, Long-term potentiation and Synaptic plasticity, Bursting and synchronization and other significant issues are discussed. We provides foundations of neuronal and network processes underlying human cognition, and then offers its application to real problems and engineering issues. Recent topics on brain-computer interface and bionics are also speculated.

 

Professor

Jeaseung Jeong (jsjeong@kaist.ac.kr, 042-350-4319)

 

Class

CMS Rm219 (Tue, Thu 9:00-10:30)

 

Credit

3 Unints (3:0:3)

 

Prerequisite

No prerequisite class

 

Grading

Mid-term exam (40%)

Final exam (40%)

Class participation (20%)

 

Office Hours

Wednesday 2:00-3:00 pm (CMS buld. #1109, 350-4319)

 

TA

Hoon-Hee Kim

 

Textbook

Fundamentals of Computational Neuroscience (Thomas P. Trappenberg, 2009)

 

Lecture Schedule

0. Introduction of class

1. What is the computational neuroscience?

2. Action potential generation

3. Spiking neurons and response variability

4. Neuronal population dynamics 

5. Information coding and decoding

6. Perceptron and self-organizing networks

7. Synaptic plasticity and Long-term potentiation

8. Mid-term Exam

9. Reverberating networks

10. Chaotic networks

11. Continuous attractor networks

12. Motor learning and control  

13. Reward learning

14. Modular mapping network

15. attentive vision and working memory